Microstructural Consequences of Blast Lung Injury Characterized with Digital Volume Correlation

Complete mechanical regularization applied to digital image and volume correlation

Digital image and volume correlation with X-ray micro-computed tomography for deformation and damage characterisation of woven fibre-reinforced composites

Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss–Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

Accurately capturing internal crack evolution and deformation is essential for understanding rock failure and improving disaster prediction. However, traditional methods mainly focus on surface cracks and struggle to reveal the spatial characteristics of crack propagation, limiting insights into rock damage mechanisms. This study proposes a framework for automatically and visually characterizing crack behavior and deformation evolution by integrating in-situ X-ray computed tomography (XCT), deep learning, and digital volume correlation (DVC) techniques to address this limitation. This framework enhances crack identification accuracy and internal deformation field analysis. First, in-situ XCT scanning experiments were conducted to dynamically capture the crack propagation process in sandstone samples with an initial single defect. Next, a U-Net-based image segmentation approach was employed to improve crack extraction accuracy from CT images, enabling precise characterization of internal crack behavior. Finally, DVC technology was utilized to compute three-dimensional (3D) full-field displacement and strain distributions, facilitating the visualization and quantitative analysis of internal rock deformation mechanisms. The results indicate that the displacement difference between monitoring points on both sides of a crack enables the quantitative classification of crack types. Crack behavior in sandstone generally exhibits a mixed combination of opening, shearing, and tearing modes. Moreover, the heterogeneous displacement field distribution within the sandstone influences spatial variations in crack behavior. After crack formation, the internal strain field exhibits significant localization, which largely governs the sample’s failure process. The distribution characteristics of the ε yy and ε xy strain fields closely correspond to the internal crack morphology, demonstrating that their evolution effectively characterizes the internal rock damage process.

Self-Adaptive Digital Volume Correlation for Unknown Deformation Fields

Digital volume correlation (DVC) is a powerful experimental tool for quantitative 3D internal deformation measurement throughout the interior of materials or biological tissues. By comparing the volumetric images acquired at the reference and deformed states by a volumetric imaging facility (e.g., X-ray CT), DVC provides full-field displacements with subvoxel accuracy and full-field strain maps. When using DVC method for internal deformation measurement, the quality of internal speckle pattern of a test sample has an important impact on the accuracy and precision of the measured displacements. In this work, theoretical analysis and numerical simulation experiments are firstly performed to assess the quality of internal speckle patterns. The results show that the displacement measurement error of DVC is negative correlation with the sum of square of subvolume intensity gradient (SSSIG) of a subvolume. Thus, the SSSIG value of a subvolume can be used as a simple and effective metric for quality assessment of its internal speckle pattern. In practical DVC analyses, increasing the SSSIG parameter of a subvolume helps to improve the measurement accuracy and precision of DVC. Despite the SSSIG in a subvolume can be increased by enlarging its subvolume size, this simple approach, however, would lead to increased calculation burden. With a purpose to increase SSSIG without significantly increase the amount of calculation, this paper further proposes a method for optimal selection of calculation voxel points in subvolumes. Specifically, the voxel points with small gray gradients in a subvolume are excluded from DVC calculation to decrease the amount of calculation when increasing the size of the subvolume. The efficacy of the presented voxel point optimal selection method is validated by analyzing computer simulated and experimentally obtained volume images. Experimental results reveal that the proposed voxel point selection method is capable of reducing the calculation burden when increasing the subvolume size to increase the SSSIG parameters.

Digital volume correlation (DVC) is a powerful technique to measure the deformation inside the material or a structure. In the DVC working steps, the displacements of points of interest (POIs) are first calculated by tracking the correspondences between the volumes acquired before and after deformation using speckle correlation, which is then followed by strain calculation using the derivative of these displacements. Generally, displacement accuracy of the POIs heavily depends on the speckle quality. Unlike the speckles on the surface of test object for digital image correlation analysis can be painted or fabricated, however, the speckles inside the material or a structure for DVC analysis are often not controllable. In addition, the volume acquired by the common volumetric imaging equipment generally has limited resolution and large image noise. As a result, the poor speckle quality and large image noise introduce great displacement outliers. Strain calculation is vulnerable to displacement noise because of the high sensitivity of differential calculation to noise. Therefore, it is critical to discard the displacement outliers to guarantee the strain calculation accuracy. In this work, I proposed a ticket voting method to detect displacement outliers and then exclude them in the strain calculation. In the ticket voting method, each POI is voted by its neighboring POIs based on the residual errors in strain calculation. If this POI obtains more than half of the total tickets, it will be included in strain calculation, otherwise excluded. Both simulation and experiment results showed that the proposed method had significantly higher accuracy than the conventional method; and our DVC method can avoid the miscalculated strain concentration.

We develop speed, efficiency, and accuracy improvements to a three-dimensional (3D) digital volume correlation (DVC) algorithm, which measures displacement and strain fields throughout the interior of a material. Our goal is to perform DVC with resolution comparable to that achieved in 2D digital image correlation, in time that is commensurate with the image acquisition time. This represents a significant improvement over the current state-of-the-art available in the literature. Using an X-ray micro-CT scanner, we can resolve features at the 5 micron scale, generating 3D images with up to 36 billion voxels. We utilize linear and quadratic shape functions with tricubic spline interpolation to achieve high accuracy. We improve the algorithm’s speed and robustness through an improved coarse search, efficient implementation of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point all grow cubically. We therefore employ parallel computing to handle this tremendous increase in computational and memory requirements. We study how various parameters affect the accuracy of the solution, and how to refine the solution to achieve improved accuracy at reduced computational cost. We demonstrate the effectiveness of our improved DVC implementation using simulated deformations of 3D micro-CT scans of polymer and ceramic foam samples.

In-situ testing capabilities of lab-scale industrial computed tomography are rapidly improving. In the present study, the compressive deformation behaviour of a flax fibre-reinforced epoxy composite is studied in an in-situ compression test with stepwise loading, during 3D imaging with X-ray Computed Tomography (XCT). The 3D volumes are processed with Digital Volume Correlation (DVC) to identify the deformation of each loading step. The natural variability of the flax texture served as an adequate speckle pattern for DVC. The precision and accuracy of the DVC were investigated prior to the analysis of the deformation volumes by digitally deforming the reference undeformed volume with a known strain of 2 %. The calculated global strain was in good agreement with the digitally applied strain. Subsequently, DVC was applied to the acquired reference and deformed volumes and the analysis was focused on the 3D strain field evolution between the consecutive loading steps. Strain localization regions were observed in the axial strain εzz and the in-plane strain εxx. In the subsequent loading steps, the onset of compressive fibre-kinking is observed as a rotating fibre inside the magnified strain region. The 3D strain field evolution allows for a better understanding of the deformation and failure mechanisms at the meso/micro-scale in natural fibre reinforced composites under compressive loads.

Mapping soil deformation around plant roots using in vivo 4D X-ray Computed Tomography and Digital Volume Correlation

DVC analysis of a polymer material subjected to tensile loading with synchrotron radiation tomography

BackgroundThe objective study was to compare medial tibial stress variations in three different situations: (1) on native knees, (2) after performing medial meniscus ramp lesions then (3) after their arthroscopic treatments with all-inside suture or total menisectomy.MethodsWe performed a biomechanical cadaveric study on specimens with a normal knee joint line alignment and without meniscal and/or cartilaginous injuries. The comparison of medial tibial stress variations was measured by a digital volume correlation (DVC) method using 7 Tesla MRI images at different loadings as close as possible to physiological conditions (1500 N).ResultsWe observed that medial tibial cartilage deformation and mechanical response, and thus the thickness of the cartilage, are restored in the case of all-inside arthroscopic suture whereas they increase in the case of menisectomy.ConclusionMedial menisectomy increases medial tibial cartilage displacement fields in physiological compression, whereas all-inside arthroscopic suture allows to get closer to the initial conditions before medial meniscus ramp lesion. This biomechanical study suggests that ramp lesions are potentially at risk of osteoarthritic evolution and that its repair would make it possible to reduce the risks associated with increased stress on the medial tibial cartilage.

Damage development during flexural loading of a 5-directional braided C/C-SiC composite, characterized by X-ray tomography and digital volume correlation

Simple SummaryThis study aimed at producing a polymeric scaffold with the ability to replicate the structure and mechanical properties of native trabecular bone. The scaffold’s morphological characteristics and mechanical parameters were compared to those of trabecular bone from bovine iliac crest. Using a three-step procedure, the elastic modulus and yield strength were investigated, and a dynamic test was performed to evaluate the mechanical behavior under various loading regimes. The local micromechanics of the scaffolds were assessed with in situ microcomputed tomography and digital volume correlation, which measured the full-field strain distribution. Overall, the results showed that the fabricated polymeric scaffold exhibited mechanical properties in the range of trabecular bone and represents a suitable bone surrogate for in vitro applications, with the potential to be further translated for in vivo clinical purposes.This study presents a polymeric scaffold designed and manufactured to mimic the structure and mechanical compressive characteristics of trabecular bone. The morphological parameters and mechanical behavior of the scaffold were studied and compared with trabecular bone from bovine iliac crest. Its mechanical properties, such as modulus of elasticity and yield strength, were studied under a three-step monotonic compressive test. Results showed that the elastic modulus of the scaffold was 329 MPa, and the one for trabecular bone reached 336 MPa. A stepwise dynamic compressive test was used to assess the behavior of samples under various loading regimes. With microcomputed tomography (µCT), a three-dimensional reconstruction of the samples was obtained, and their porosity was estimated as 80% for the polymeric scaffold and 88% for trabecular bone. The full-field strain distribution of the samples was measured using in situ µCT mechanics and digital volume correlation (DVC). This provided information on the local microdeformation mechanism of the scaffolds when compared to that of the tissue. The comprehensive results illustrate the potential of the fabricated scaffolds as biomechanical templates for in vitro studies. Furthermore, there is potential for extending this structure and fabrication methodology to incorporate suitable biocompatible materials for both in vitro and in vivo clinical applications.

The purpose of this study is to measure three-dimensional deformation of the porcine knee joint articular cartilage under compressive load by using digital volume correlation. The compression tester is construed for collecting the micro-CT images before and after the loading. The compressive load is applied to the test sample, and the compressive load is measured by a load cell. The micro-CT scanning is conducted, all of which are imported images. The three-dimensional displacement distribution and strain distribution of the articular cartilage are obtained using digital volume correlation. By examining the relationship between the compressive load and the strain distribution, the possibility of measuring the deformation characteristics of articular cartilage by using this method with minimum invasive is discussed.KeywordsDigital volume correlationMicro-CTKnee cartilageStrain distribution